The basic concept of personalized medicine is to tailor the treatment for a patient based on his or her genetic makeup, clinical conditions and other personal characteristics to improve efficacy and safety. The coming of the big data era enables us to characterize individual in fine pictures and make the "personalized" clinical decision truly personalized. Such an approach has great potential for improving disease prevention, diagnosis and treatment. For example, in a typical randomized clinical trial aiming for proving the efficacy of a treatment, the final conclusion is drawn based on the average treatment effect in the entire study population. It is possible that while the average treatment effect is near null, the treatment may still be beneficial to a subgroup of patients whose identification prior to the treatment is thus very important. The overall objective of statistical analysis in this area is to provide a data-based empirical estimator for the personalized treatment effect, which can be used to identify subgroup of patients who may benefit the most from a treatment. In this study, we first propose to develop robust statistical methods for estimating the group-specific treatment effect. The proposed approach incorporating many existing methods as special cases depends on minimum model assumptions and provides a general framework for generalization and improvement. We will also discuss how to use the estimated personalized treatment effect to stratify patient population into clinically meaningful strata for better assisting the decision making of clinicians. Secondly, we will study a regularize principal components analysis method for dimension reduction in structured high-dimensional data. The output from the analysis can be used to summarize the characteristics of individual patient as well as for predicting future clinical outcomes of interest. Multiple methods can be used to estimate the treatment effect and form the corresponding treatment selection strategy. Therefore it is important to evaluate and compare the performance of such strategies. Thus our last aim is to develop a systematic robust procedure for evaluating the performance of the personalized treatment effect estimation and associated treatment selection strategy.

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For a given treatment, the effect may be very different for different patients. Therefore it is important to develop methods predicting the personalized treatment effect and discovering subgroup of patients who may benefit from a treatment. In this proposal, we plan to study the statistical methods to help achieve these important goals by analyzing empirical data.

National Institute of Health (NIH)
Research Project (R01)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Wolz, Michael
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Stanford University
Schools of Medicine
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Matsouaka, Roland A; Li, Junlong; Cai, Tianxi (2014) Evaluating marker-guided treatment selection strategies. Biometrics 70:489-99
Parast, Layla; Tian, Lu; Cai, Tianxi (2014) Landmark Estimation of Survival and Treatment Effect in a Randomized Clinical Trial. J Am Stat Assoc 109:384-394
Tian, Lu; Zhao, Lihui; Wei, L J (2014) Predicting the restricted mean event time with the subject's baseline covariates in survival analysis. Biostatistics 15:222-33
Uno, Hajime; Claggett, Brian; Tian, Lu et al. (2014) Moving beyond the hazard ratio in quantifying the between-group difference in survival analysis. J Clin Oncol 32:2380-5
Seok, Junhee; Tian, Lu; Wong, Wing H (2014) Density estimation on multivariate censored data with optional Polya tree. Biostatistics 15:182-95
Zhao, Lihui; Tian, Lu; Cai, Tianxi et al. (2013) EFFECTIVELY SELECTING A TARGET POPULATION FOR A FUTURE COMPARATIVE STUDY. J Am Stat Assoc 108:527-539
Zheng, Yingye; Cai, Tianxi; Pepe, Margaret S (2013) Adopting nested case-control quota sampling designs for the evaluation of risk markers. Lifetime Data Anal 19:568-88
Zhou, Qian M; Zheng, Yingye; Cai, Tianxi (2013) Subgroup specific incremental value of new markers for risk prediction. Lifetime Data Anal 19:142-69
Tian, Lu; Cai, Tianxi; Zhao, Lihui et al. (2012) On the covariate-adjusted estimation for an overall treatment difference with data from a randomized comparative clinical trial. Biostatistics 13:256-73
Cai, Tianxi; Tian, Lu; Wong, Peggy H et al. (2011) Analysis of randomized comparative clinical trial data for personalized treatment selections. Biostatistics 12:270-82

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